A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report
generated on 2025-07-01, 10:24 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant.URL: https://github.com/bigbio/pmultiqc
Summary and HeatMap
Summary Table
This plot shows the summary statistics of the submitted data.
This plot shows the summary statistics of the submitted data.
Showing 1/1 rows and 4/4 columns.
| #MS2 Spectra | #Identified MS2 Spectra | %Identified MS2 Spectra | #Peptides Identified | #Proteins Identified |
|---|---|---|---|---|
| 117692 | 116753 | 99.20% | 22309 | 4532 |
HeatMap
This heatmap provides an overview of the performance of the quantms.
This plot shows the pipeline performance overview. Some metrics are calculated.
* Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants;
1 = no contaminants
* Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold,
i.e. reaching 2^21 of 2^23 gives score 0.25.
* Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative
Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at
tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?),
in-source fragmentation, missed cleavages and buffer irregularities can cause a shift
(see Bittremieux 2017, DOI: 10.1002/mas.21544).
* Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
* Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion
state of the current study.
* Heatmap score [ID rate over RT]: Judge column occupancy over retention time.
Ideally, the LC gradient is chosen such that the number of identifications
(here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles.
Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient
optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
* Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled
3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was
identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples,
oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks,
reducing the number of identified peptides.
* Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
Created with MultiQC
Pipeline Result Statistics
This plot shows the submitted results
This plot shows the submitted results.
Including the number of identified peptides and the number of identified modified peptides in the submitted results.
You can also remove the decoy with the `remove_decoy` parameter.
Showing 1/1 rows and 4/4 columns.
| Spectra File | #Peptide IDs | #Unambiguous Peptide IDs | #Modified Peptide IDs | #Protein (group) IDs |
|---|---|---|---|---|
| 33060_Control_vs_Infection_JMI | 25008 | 23684 | 6011 | 4532 |
Identification Summary
Number of Peptides identified Per Protein
This plot shows the number of peptides per protein in the submitted data
Proteins supported by more peptide identifications can constitute more confident results.
MS2 and Spectral Stats
Number of Peaks per MS/MS spectrum
This chart represents a histogram containing the number of peaks per MS/MS spectrum
in a given experiment.
This chart assumes centroid data. Too few peaks can identify poor fragmentation
or a detector fault, as opposed to a large number of peaks representing very noisy spectra.
This chart is extensively dependent on the pre-processing steps performed to the spectra
(centroiding, deconvolution, peak picking approach, etc).
Peak Intensity Distribution
This is a histogram representing the ion intensity vs.
the frequency for all MS2 spectra in a whole given experiment.
It is possible to filter the information for all, identified and unidentified spectra.
This plot can give a general estimation of the noise level of the spectra.
Generally, one should expect to have a high number of low intensity noise peaks with a low number
of high intensity signal peaks.
A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or
potential experimental problems. In the case of data reuse this plot can be useful in
identifying the requirement for pre-processing of the spectra prior to any downstream analysis.
The quality of the identifications is not linked to this data as most search engines perform internal
spectrum pre-processing before matching the spectra. Thus, the spectra reported are not
necessarily pre-processed since the search engine may have applied the pre-processing step
internally. This pre-processing is not necessarily reported in the experimental metadata.
Distribution of Precursor Charges
This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
This information can be used to identify potential ionization problems
including many 1+ charges from an ESI ionization source or an unexpected
distribution of charges. MALDI experiments are expected to contain almost exclusively 1+
charged ions. An unexpected charge distribution may furthermore be caused by specific search
engine parameter settings such as limiting the search to specific ion charges.
MS/MS Counts Per 3D-peak
An oversampled 3D-peak is defined as a peak whose peptide ion
(same sequence and same charge state) was identified by at least two distinct MS2 spectra
in the same Raw file.
For high complexity samples, oversampling of individual 3D-peaks automatically leads to
undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic
exclusion windows for data independent acquisitions.
